Abstract: A device includes a machine-learning audio encoder and a waveform-matching audio encoder. The device includes a controller configured to cause a segment of audio data to be input to the machine-learning audio encoder, to the waveform-matching audio encoder, or to both, based on a classification associated with the segment.

Abstract: A device includes a neural network, a first subband neural network, a second subband neural network, and a reconstructor. The neural network processes neural network inputs to generate a neural network output. The neural network inputs include at least one previous audio sample. The first subband neural network processes first subband network inputs to generate a first subband audio sample. The first subband network inputs include at least the neural network output. The second subband neural network processes second subband network inputs to generate a second subband audio sample. The second subband network inputs include at least the neural network output. The reconstructor generates a reconstructed audio sample based on the first subband audio sample and the second subband audio sample. The at least one previous audio sample includes a previous subband audio sample, a previous reconstructed audio sample, or both.

Abstract: A device includes a memory and one or more processors configured to process sensor data to determine a semantical context associated with the sensor data. The one or more processors are also configured to generate adjusted face data based on the determined semantical context and face data. The adjusted face data includes an avatar facial expression that corresponds to the semantical context.

Abstract: A device includes a memory configured to store a collection of sets of weights, each of the sets of weights representing a respective media segment. The device also includes one or more processors configured to generate data representing the detected first input speech segment and to pass the data representing the detected first input speech segment into a collection of memory units. Each memory unit of the collection of memory units includes a set of weights from the collection of sets of weights. The one or more processors are also configured to generate a first estimate of an associated media segment that represents the detected first input speech segment. The associated media segment corresponds to a first memory unit in the collection of memory units.

Abstract: A device includes one or more processors configured to input one or more segments of an input media stream into a feature extractor. The one or more processors are further configured to pass an output of the feature extractor into an utterance classifier to produce at least one representation of at least one utterance class of a plurality of utterance classes. The one or more processors are further configured to pass the output of the feature extractor and the at least one representation into a segment matcher to produce a media output segment identifier.

Abstract: A device includes a memory configured to store a collection of sets of weights, each of the sets of weights representing a respective media segment. The device also includes one or more processors configured to generate data representing the detected first input speech segment and to pass the data representing the detected first input speech segment into a collection of memory units. Each memory unit of the collection of memory units includes a set of weights from the collection of sets of weights. The one or more processors are also configured to generate a first estimate of an associated media segment that represents the detected first input speech segment. The associated media segment corresponds to a first memory unit in the collection of memory units.

Abstract: A device includes one or more processors configured to input one or more segments of an input media stream into a feature extractor. The one or more processors are further configured to pass an output of the feature extractor into an utterance classifier to produce at least one representation of at least one utterance class of a plurality of utterance classes. The one or more processors are further configured to pass the output of the feature extractor and the at least one representation into a segment matcher to produce a media output segment identifier.

Abstract: Systems and techniques are described herein for encoding and/or decoding audio information. For example, a process can process an input audio segment to generate a representation of the input audio segment, and can compare the representation of the input audio segment to representations stored in a memory. The representations represent a plurality of audio segments. The process can determine, based on the comparison, target representation(s) of target audio segment(s) from the representations stored in the memory. The process can determine one or more indices associated with the target audio segment(s). The process can then packetize the one or more indices and transmit the one or more packetized indices (e.g., to a decoder configured to decode the packetized indices).

Abstract: A device includes a memory and one or more processors configured to process sensor data to determine a semantical context associated with the sensor data. The one or more processors are also configured to generate adjusted face data based on the determined semantical context and face data. The adjusted face data includes an avatar facial expression that corresponds to the semantical context.

Abstract: A device includes a memory and one or more processors configured to process image data corresponding to a user's face to generate face data. The one or more processors are configured to process sensor data to generate feature data and to generate a representation of an avatar based on the face data and the feature data. The one or more processors are also configured to generate an audio output for the avatar based on the sensor data.

Abstract: A system for transmitting input data over a speech channel of a network comprising: a modulator arranged to produce a modulated waveform signal transforming the data for transmission over the network; a channel compensation filter arranged to filter the modulated waveform signal after it has been transmitted over the speech channel to compensate for the response of the speech channel; and a demodulator arranged to retrieve the data from the filtered waveform signal.

Abstract: To alleviate problems of signal aliasing and to reduce complexity, Linear Predictive Coefficients (LPCS) are calculated from samples of audio signals and Line Spectral Frequency (LSF) vectors are extracted from the LPCs with a rate higher than a desired vector rate, the LSF vectors comprising values of different LSF parameters. Next, an LSF track is formed for at least one of the LSF parameters. At least one of the formed LSF tracks is then low pass filtered. Finally, decimated LSF vectors are reconstructed from the low pass filtered LSF tracks, the decimated number corresponding to the desired vector rate. The invention equally relates to a corresponding computer program, to corresponding devices and to a corresponding communication network.

Abstract: A system for transmitting input data over a speech channel of a network comprising: a modulator arranged to produce a modulated waveform signal transforming the data for transmission over the network; a channel compensation filter arranged to filter the modulated waveform signal after it has been transmitted over the speech channel to compensate for the response of the speech channel; and a demodulator arranged to retrieve the data from the filtered waveform signal.

Abstract: To alleviate problems of signal aliasing and to reduce complexity, Linear Predictive Coefficients (LPCS) are calculated from samples of audio signals and Line Spectral Frequency (LSF) vectors are extracted from the LPCs with a rate higher than a desired vector rate, the LSF vectors comprising values of different LSF parameters. Next, an LSF track is formed for at least one of the LSF parameters. At least one of the formed LSF tracks is then low pass filtered. Finally, decimated LSF vectors are reconstructed from the low pass filtered LSF tracks, the decimated number corresponding to the desired vector rate. The invention equally relates to a corresponding computer program, to corresponding devices and to a corresponding communication network.